本地部署Whisper实现语言转文字

文章目录

• 本地部署Whisper实现语言转文字
• 1.前置条件
• 2.安装chocolatey
• 3.安装ffmpeg
• 4.安装whisper
• 5.测试用例
• 6.命令行用法
• 7.本地硬件受限，借用hugging face资源进行转译

  本地部署Whisper实现语言转文字

  1.前置条件

  环境windows10 64位

  2.安装chocolatey

  安装chocolatey目的是安装ffpeg

  以管理员身份运行PowerShell

  粘贴命令

  Set-ExecutionPolicy Bypass -Scope Process -Force; [System.Net.ServicePointManager]::SecurityProtocol = [System.Net.ServicePointManager]::SecurityProtocol -bor 3072; iex ((New-Object System.Net.WebClient).DownloadString('https://community.chocolatey.org/install.ps1'))

  安装成功打入choco

  安装文件夹路径

  C:\ProgramData\chocolatey

  3.安装ffmpeg

  choco install ffmpeg

  4.安装whisper

  pip install git+https://github.com/openai/whisper.git

  安装完成运行

  pip install --upgrade --no-deps --force-reinstall git+https://github.com/openai/whisper.git

  安装完成

  5.测试用例

  直接命令行

  whisper yoump3.mp3

  6.命令行用法

  以下命令将使用medium模型转录音频文件中的语音：

  whisper audio.flac audio.mp3 audio.wav --model medium

  默认设置（选择模型small）非常适合转录英语。要转录包含非英语语音的音频文件，您可以使用以下选项指定语言--language：

  whisper japanese.wav --language Japanese

  添加--task translate后将把演讲翻译成英文：

  whisper japanese.wav --language Japanese --task translate

  运行以下命令查看所有可用选项：

  whisper --help

  7.本地硬件受限，借用hugging face资源进行转译

  进入huggingface网址，往下拉

  https://huggingface.co/openai/whisper-large-v3

  粘贴上述代码

  import torchfrom transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipelinefrom datasets import load_datasetdevice = "cuda:0" if torch.cuda.is_available() else "cpu"torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32model_id = "openai/whisper-large-v3"model = AutoModelForSpeechSeq2Seq.from_pretrained(    model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True, use_safetensors=True)model.to(device)processor = AutoProcessor.from_pretrained(model_id)pipe = pipeline(    "automatic-speech-recognition",    model=model,    tokenizer=processor.tokenizer,    feature_extractor=processor.feature_extractor,    max_new_tokens=128,    chunk_length_s=30,    batch_size=16,    return_timestamps=True,    torch_dtype=torch_dtype,    device=device,)dataset = load_dataset("distil-whisper/librispeech_long", "clean", split="validation")sample = dataset[0]["audio"]result = pipe(sample)print(result["text"])

  修改本地代码，将sample修改为，需要转录的录音，接入代理；

  import torchfrom transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipelinefrom datasets import load_datasetimport osos.environ['CURL_CA_BUNDLE'] = ''os.environ["http_proxy"] = "http://127.0.0.1:7890"os.environ["https_proxy"] = "http://127.0.0.1:7890"device = "cuda:0" if torch.cuda.is_available() else "cpu"torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32model_id = "openai/whisper-large-v3"model = AutoModelForSpeechSeq2Seq.from_pretrained(    model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True, use_safetensors=True)model.to(device)processor = AutoProcessor.from_pretrained(model_id)pipe = pipeline(    "automatic-speech-recognition",    model=model,    tokenizer=processor.tokenizer,    feature_extractor=processor.feature_extractor,    max_new_tokens=128,    chunk_length_s=30,    batch_size=16,    return_timestamps=True,    torch_dtype=torch_dtype,    device=device,)dataset = load_dataset("distil-whisper/librispeech_long", "clean", split="validation")sample = dataset[0]["audio"]result = pipe("myaudio")print(result["text"])

  借用huggingface的速度，速度取决于网速